Application of sampling methodologies to network traffic characterization
SIGCOMM '93 Conference proceedings on Communications architectures, protocols and applications
End-to-end routing behavior in the Internet
Conference proceedings on Applications, technologies, architectures, and protocols for computer communications
Trajectory sampling for direct traffic observation
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Adaptive random sampling for load change detection
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Machine Learning
Adaptive Sampling for Network Management
Journal of Network and Systems Management
Properties and prediction of flow statistics from sampled packet streams
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Adaptive Sampling Methods to Determine Network Traffic Statistics including the Hurst Parameter
LCN '98 Proceedings of the 23rd Annual IEEE Conference on Local Computer Networks
Identifying elephant flows through periodically sampled packets
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Automated Traffic Classification and Application Identification using Machine Learning
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Traffic classification on the fly
ACM SIGCOMM Computer Communication Review
ACM SIGCOMM Computer Communication Review
Fisher information of sampled packets: an application to flow size estimation
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Impact of packet sampling on anomaly detection metrics
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Is sampled data sufficient for anomaly detection?
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Offline/realtime traffic classification using semi-supervised learning
Performance Evaluation
Lightweight application classification for network management
Proceedings of the 2007 SIGCOMM workshop on Internet network management
Passive analysis of TCP anomalies
Computer Networks: The International Journal of Computer and Telecommunications Networking
Portscan Detection with Sampled NetFlow
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
TIE: A Community-Oriented Traffic Classification Platform
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
GT: picking up the truth from the ground for internet traffic
ACM SIGCOMM Computer Communication Review
Deterministic versus probabilistic packet sampling in the internet
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
Analysis of the impact of sampling on NetFlow traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
KISS: stochastic packet inspection classifier for UDP traffic
IEEE/ACM Transactions on Networking (TON)
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The use of packet sampling for traffic measurement has become mandatory for network operators to cope with the huge amount of data transmitted in today's networks, powered by increasingly faster transmission technologies. Therefore, many networking tasks must already deal with such reduced data, more available but less rich in information. In this work we assess the impact of packet sampling on various network monitoring-activities, with a particular focus on traffic characterization and classification. We process an extremely heterogeneous dataset composed of four packet-level traces (representative of different access technologies and operational environments) with a traffic monitor able to apply different sampling policies and rates to the traffic and extract several features both in aggregated and per-flow fashion, providing empirical evidences of the impact of packet sampling on both traffic measurement and traffic classification. First, we analyze feature distortion, quantified by means of two statistical metrics: most features appear already deteriorated under low sampling step, no matter the sampling policy, while only a few remain consistent under harsh sampling conditions, which may even cause some artifacts, undermining the correctness of measurements. Second, we evaluate the performance of traffic classification under sampling. The information content of features, even though deteriorated, still allows a good classification accuracy, provided that the classifier is trained with data obtained at the same sampling rate of the target data. The accuracy is also due to a thoughtful choice of a smart sampling policy which biases the sampling towards packets carrying the most useful information. Copyright © 2012 John Wiley & Sons, Ltd.